[2] Binhang Yuan, Song Ge, and Wenhui Xing. A federated learning framework for healthcare IoT devices. arXiv:2005.05083, 2020.
[3] Bekir Sait Ciftler, Abdullahif Albaseer, Noureddine Lasla, and Mohamed Abdallah. Federated learning for localization: A privacy-preserving crowdsourcing method. arXiv:2001.01911, 2020.
[4] Jason Posner, Lewis Tseng, Moayad Aloqaily, and Yaser Jararweh. Federated learning in vehicular networks: Opportunities and solutions. IEEE Network, 2021.
[5] Boyi Liu, Lujia Wang, and Ming Liu. Lifelong federated reinforcement learning: a learning architecture for navigation in cloud robotic systems. IEEE Robotics and Automation Letters, 4(4):4555-4562, 2019.
[6] Boyi Liu, Lujia Wang, Ming Liu, and Cheng-Zhong Xu. Federated imitation learning: A privacy considered imitation learning framework for cloud robotic systems with heterogeneous sensor data. arXiv:1909.00895, 2019.
[7] Farzad Samie, Vasileios Tsoutsouras, Lars Bauer, Sotirios Xydis, Dimitrios Soudris, and Jörg Henkel. Computation offloading and resource allocation for low-power iot edge devices. In 2016 IEEE 3rd world forum on internet of things (WF-IoT), pages 7-12. IEEE, 2016.
[8] Jed Mills, Jia Hu, and Geyong Min. Communication-efficient federated learning for wireless edge intelligence in iot. IEEE Internet of Things Journal, 7(7):5986-5994, 2019.
[9] Sebastian Caldas, Jakub Konečny, H Brendan McMahan, and Ameet Talwalkar. Expanding the reach of federated learning by reducing client resource requirements. arXiv:1812.07210, 2018.
[10] Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, and Raman Arora. Fetchsgd: Communication-efficient federated learning with sketching. In International Conference on Machine Learning, pages 8253-8265. PMLR, 2020.
[11] Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. Federated optimization in heterogeneous networks. In Proceedings of Machine Learning and Systems, volume 2, pages 429-450, 2020.
[12] Y. Chen, Y. Ning, M. Slawski, and H. Rangwala. Asynchronous online federated learning for edge devices with non-iid data. In 2020 IEEE International Conference on Big Data (Big Data), pages 15-24, 2020.
[13] Cong Xie, Sanmi Koyejo, and Indranil Gupta. Asynchronous federated optimization. arXiv:1903.03934, 2020.
[14] Zheng Chai, Yujing Chen, Liang Zhao, Yue Cheng, and Huzefa Rangwala. Fedat: A communication-efficient federated learning method with asynchronous tiers under non-iid data. arXiv:2010.05958, 2020.
[15] Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, and Yue Cheng. Tifl: A tier-based federated learning system. In Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing, HPDC '20, page 125–136, New York, NY, USA, 2020. Association for Computing Machinery.
[16] Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays. Applied federated learning: Improving google keyboard query suggestions. arXiv:1812.02903, 2018.
[17] Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, and Carole-Jean Wu. Towards fair federated recommendation learning: Characterizing the inter-dependence of system and data heterogeneity. In Proceedings of the 16th ACM Conference on Recommender Systems, pages 156-167, 2022.